Basics and Recent Applications of Machine Learning in Physics Problems

In this presentation, I will first introduce the basics of machine learning. I will explain key concepts such as data, features, supervised and unsupervised learning, loss functions, overfitting, and generalization, along with a brief introduction to linear regression and neural networks. In the second part, I will give an overview of recent applications of cutting-edge machine learning techniques to physics problems, including generative models for physics data, physics-informed deep learning, and interpretable and explainable machine learning methods.